Circulating white blood cells affect red cell pulmonary transit times in endurance athletes during intense exercise
Bibliographic record
Abstract
PURPOSE: The aim of this study was to determine the relationship between the right-to-left ventricular red cell pulmonary transit times (PTT) during intense exercise and circulating white blood cell (WBC) counts in highly trained endurance athletes. We postulated that high levels of WBCs preexercise would slow PTT. Eleven endurance-trained athletes (VO2max = 69.6 +/- 7.7 mL.kg-1.min-1; weight = 75.0 +/- 6.2 kg; height = 181.0 +/- 7.1 cm) performed 6.5 min constant-load, near-maximal cycling exercise (approximately 92% VO2max) on two different days. Preexercise WBC counts were measured in arterial blood drawn from the radial artery 30 min before exercise. PTT was measured during the 3rd min of exercise by first-pass radionuclide cardiography using centroid and deconvolution analysis, whereas cardiac output (Q) was measured during the last 2.5 min of exercise via a count-based ratio method from the MUGA technique. RESULTS: Combined mean PTT from both deconvolution and centroid analysis at minute three of exercise was 2.45 +/- 0.21 s, whereas the preexercise WBC count was 5.3 +/- 1.6 x 109.L-1. Cardiopulmonary blood volume at minute three of exercise was 1.22 +/- 0.13 L, VO2 was 4.58 +/- 0.44 L.min-1, and Q was 30.2 +/- 4.2 L.min-1. We found that PTT was negatively correlated with circulating WBC (r = -0.61; adjusted r2 = 0.30; P = 0.04; N = 11) but not with the dispersion (spread) of transit times around the mean (r = 0.19; P = 0.57). CONCLUSION: This suggests that athletes with higher circulating numbers of WBCs preexercise have faster (shorter) red cell transit times through the lung during intense exercise.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".